Method for Detecting Airplane Flight Events, Mobile Communication Device, and Computational Unit Therefor

ABSTRACT

A method for detecting airplane flight events using at least one acceleration sensor associated with a mobile communication device includes obtaining at least one acceleration signal from the at least one acceleration sensor; pre-processing the obtained at least one acceleration signal to remove redundant information present in the at least one acceleration signal; performing a feature extraction on the pre-processed at least one acceleration signal; and classifying airplane flight events based on an acceleration pattern represented by the extracted acceleration features.

CROSS REFERENCE TO PRIOR APPLICATION

This patent application claims priority to EP 09 16 7795.5, filed onAug. 13, 2009, which is incorporated by reference in its entiretyherein.

The invention relates to a system which is installed in airplane cargo,and detects taking off and landing using accelerometer sensors. Thissystem then switches off the cargo GSM devices for taking off, andswitches them on after the flight has landed.

BACKGROUND

CN-A-1630232 describes a discrete tracking system and method, whichinstalls an RF card or barcode reader-writer in a discrete distributedmaterials circulation place. The RF card reader-writer reads out theinformation in RF card when materials circulation reaches or passesmaterials circulation place, resulting in obtaining the goods discretetracking information. The tracking information is then transmitted toanyone who needs the information through GSM, GPRS, internet andintranet etc.

WO 01/033247 describes a system for determining the position ofcommunication units for mounting on vehicles, containers or the like.Both the communication unit and the mobile unit may be provided withdetectors, which may detect activity in the surroundings.

US 2009/0015400 describes a remotely monitorable shipping containerassembly including a shipping container including at least one door, adoor status sensor for monitoring the open or closed status of thedoor(s) and a communications device mounted on the container andwirelessly transmitting information to one or more remote facilitiesincluding the status of the door(s) as monitored by the door statussensor.

SUMMARY

In an embodiment, the present invention provides a method for detectingairplane flight events using at least one acceleration sensor associatedwith a mobile communication device. The method includes the steps of:obtaining at least one acceleration signal from said at least oneacceleration sensor, pre-processing said obtained at least oneacceleration signal to remove redundant information present in said atleast one acceleration signal, performing a feature extraction on saidpre-processed at least one acceleration signal, and classifying airplaneflight events based on an acceleration pattern represented by theextracted acceleration features.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is described in greater detail below with reference to theaccompanying drawings.

FIG. 1 is a simplified view of the system according to the invention;and

FIG. 2 is an example of an acceleration magnitude pattern during aflight.

DETAILED DESCRIPTION

Using GSM communication devices during landing and take off of anairplane can potentially cause interference with flight navigationsystems. Air cargo devices usually use GSM devices to communicate withcargo central management. When the cargo is loaded into the airplane,and the plane begins taking off, the GSM devices should be switched offto avoid interference with flight navigation systems. On the contrary,when the flight has landed completely, the GSM devices should be turnedon again to allow communication with the central cargo system.

In an embodiment, the present invention provides a mobile communicationdevice including a processor and at least one acceleration sensor. Theleast one acceleration sensor provides at least one acceleration signalto the processor. The processor is configured for pre-processing said atleast one acceleration signal to remove redundant information present insaid at least one acceleration signal. The processor is furtherconfigured for performing a feature extraction on said pre-processed atleast one acceleration signal, and the processor is configured forclassifying airplane flight events based on an acceleration patternrepresented by the extracted acceleration features.

In another embodiment, the present invention provides a computationalunit connectable with a mobile communication device; the computationalunit is for detecting airplane flight events, preferably taking offand/or landing, and comprises a processor, at least one accelerationsensor, and a controller. The at least one acceleration sensor providesat least one acceleration signal to the processor. The processor isconfigured for pre-processing said at least one acceleration signal toremove redundant information present in said at least one accelerationsignal, the processor is further configured for performing a featureextraction on said pre-processed at least one acceleration signal, andthe processor isg configured for classifying airplane flight eventsbased on an acceleration pattern represented by the extractedacceleration features. The controller is arranged to receive informationfrom the processor and to initiate transmission of a control command tothe mobile communication device based on the detected airplane flightevent once connected with the computational unit.

The present invention provides a system to automatically detect take offand landing of an airplane using accelerometer sensors, and to switchon/off the GSM devices accordingly. The acceleration sensor is, forexample, installed in a computational unit in the cargo carrier orcontainer, with a wired link to a cargo GSM system. The sensors capturethe pattern of acceleration during different cargo steps (taxiing,loading, unloading, and flying). The sensor data is processed using thecomputational unit. The computational unit uses a signal processing andmachine learning algorithm to analyse data captured by the accelerationsensor(s) and detect the moment that flight starts taking off, and themoment the flight has landed and is ready for unloading. The computationunit then sends commands for switching on/off the GSM devicesaccordingly.

According to a first aspect, the invention provides a method fordetecting airplane flight events, preferably taking off and/or landing,using at least one acceleration sensor associated with a mobile, GSM,communication device, the method comprising the steps of: obtaining atleast one acceleration signal from said at least one accelerationsensor; pre-processing said obtained at least one acceleration signal toremove redundant information present in said signal(s); performing afeature extraction on said pre-processed at least one accelerationsignal; and classifying airplane flight events based on the accelerationpattern represented by the extracted acceleration features.

As mentioned above, the term “airplane flight events” is according tothe invention not limited to ‘landing’ or ‘take off’ but alsoencompasses events in the context of a flight, such as taxiing, loading,or unloading. Each of these events can be detected according to theinvention due to the associated discriminative pattern.

According to the invention, all the sensory and processing units areintegrated in a computational unit or a mobile communication devicewhich makes the invention user friendly and inexpensive.

The step of classifying airplane flight events preferably comprisescomparing said acceleration pattern with statistical reference modelsfor events during a flight. The statistical model is preferably anartificial neural network or a Gaussian mixture model. Alternatively,the step of classifying airplane flight events comprises comparing saidacceleration pattern with a Hidden Markov model, wherein each state ofthe model models an event class.

The features extracted from said pre-processed at least one accelerationsignal are preferably selected from the group comprising: accelerationmagnitude over different axes, the rate of change in acceleration overdifferent axis, the absolute magnitude of the acceleration, and pairwisedifference between acceleration magnitudes over different axis.

It is preferred that the method further comprises the step of changingan operating state of the mobile communication device depending on theclassification result. Preferably, the mobile communication device isautomatically turned on or off. In particular, the mobile communicationdevice is automatically turned off if it is detected that the airplaneis going to take off, or the mobile communication device isautomatically turned on if it is detected that the airplane has beenlanded.

According to a preferred aspect, the method further comprises the stepof comparing the detected airplane flight event with a regular airplaneflight event. Preferably, taking off or landing is detected if theresult of the comparing step is a match below a predetermined thresholdbetween the detected airplane flight event and the one or more regularairplane flight events.

It is also preferred according to the invention that the statisticalreference models are trained for typical airplane flight events exceptfor taking off and landing.

According to a second aspect, the invention provides a mobilecommunication device comprising a processor; and at least oneacceleration sensor providing at least one acceleration signal to theprocessor; the processor being configured for pre-processing said atleast one acceleration signal to remove redundant information present insaid signals, being further configured for performing a featureextraction on said pre-processed at least one acceleration signal; andbeing configured for classifying airplane flight events based on theacceleration pattern represented by the extracted acceleration features.

Thus, in more general terms, the invention provides a method usingaccelerometer sensor(s) integrated in a mobile communication device fordetecting airplane flight events. The acceleration sensor output isfirst pre-processed to remove redundant information and extract featureswhich represent the airplane flight event in a discriminative way. Thesefeatures are used to create reference statistical models for differentactivities. The outcome of the statistical models is then used in adecision tree in order to detect current airplane flight event. Thisallows recognizing and distinguishing certain events such as loading,taxiing, take off, flying, landing, and unloading.

During the test of the system, actual samples of acceleration data(after feature extraction) are presented to the trained referencemodels. A score is estimated based on the match between ongoing samplesand reference models for different events. This score is used as a basisfor classification of different events.

The present invention encompasses several applications of detectingairplane flight events. According to one preferred embodiment, it isused to automatically turn on/off the mobile communication devicedepending on the current event.

According to a third aspect, the invention provides an independentcomputational unit connectable with a mobile communication device, thecomputational unit for detecting airplane flight events, preferablytaking off and/or landing, and comprising: a processor; at least oneacceleration sensor providing at least one acceleration signal to theprocessor; the processor being configured for pre-processing said atleast one acceleration signal to remove redundant information present insaid signals, being further configured for performing a featureextraction on said pre-processed at least one acceleration signal; andbeing configured for classifying airplane flight events based on theacceleration pattern represented by the extracted acceleration features;and a controller arranged to receive information from the processor andto initiate transmission of a control command to the mobilecommunication device once connected with the computational unit, basedon the detected airplane flight event.

According to a fourth aspect, the invention provides an air cargocarrier or container comprising a mobile communication device and/or acomputational unit according to the invention.

The computational unit is separable from the mobile communication deviceor from the air cargo carrier. Preferably, the computational unit isindependent from the mobile communication device and from the air cargocarrier. The term “separable” in accordance with the present inventionmeans that the computational unit can be physically separated from themobile communication device or from the air cargo carrier. The term“independent” in accordance with certain embodiments of the presentinvention means that the computational unit is functionally independentfrom the mobile communication device or from the air cargo carrier.

The invention is advantageous and superior to known implementationsbecause, according to the second aspect of the invention, it fits into aregular mobile communication device, and is able to operate with thelimited resources available in the mobile communication devices in termsof sensory and hardware components. The invention is also advantageousbecause according to the third aspect it can be provided in acomputational unit which is connectable to existing mobile communicationdevices of air cargo carriers or containers, for example.

According to the invention, data is captured using at least oneacceleration sensor integrated in the computational unit or the mobilecommunication device. The acceleration sensor raw data are stored in abuffer memory, pre-processed by digital filters to remove noise, andused to extract features which represent acceleration modalities morediscriminatively. Extracted features are then used in an airplane orflight event classification component to classify the ongoing scenario.It should be noted that the feature extraction and classificationmodules are preferably implemented using computational resources of themobile communication device.

The acceleration sensor(s) is(are) integrated in the mobilecommunication device and capture(s) linear acceleration in x, y and zdirections.

The feature extraction modules receiving the detected signals from theacceleration sensor(s) disregard redundant information and provide amodified or refined representation of acceleration signals which is morediscriminative for classifying ongoing activity and context.

Acceleration based features are features extracted from theaccelerometer data and are mainly acceleration magnitude over differentaxis, the rate of change in acceleration (over different axis), theabsolute magnitude of the acceleration, and pairwise difference betweenacceleration magnitudes over different axis.

In the next step, airplane or fight event classification is performed.Different events have different acceleration pattern signaturesassociated with them. The classification module receives featuresextracted from acceleration data, and builds statistical referencemodels for different events during a training phase. The training phasecan be done at a manufacturer company, and does not need to benecessarily done by the final user. During the training phase, severalfeature samples of each event class are presented to the statisticalmodel. The statistical model can be an artificial neural network orGaussian mixture models. Parameters of the statistical model are trainedaccording to the samples presented for each context class, in order tocover the feature space corresponding to each class. Such a trainedmodel can then be used to estimate scores for different event classesduring testing of the system. According to a preferred embodiment of theinvention where take off and/or landing is of interest, all events aretrained except for taking off and landing. Thus, the class having thelowest score or match is representative of an event ‘taking off’ or‘landing.’

In order to capture the information existing over time or sequence ofevents, the statistical model can be replaced with a Hidden Markov model(HMM). In this case, each state of the HMM models a certain event class,and transition probabilities between different states represent thepossibilities for transition between different event classes. Forinstance, the ‘taking off’ event should be preceded usually by a‘taxiing’ event, as the airplane usually needs to taxi from the terminalto the runway. In addition, heuristically designed binary decision treescan be used on top of the classification results to further smooth outthe decisions about the ongoing event by integrating some priorknowledge related to duration and timing of different events.

According to a preferred embodiment of the invention, extra informationsuch as location information (provided by GPS, for example) isadditionally integrated in the decision making process. Thus, it ispossible to associate different events with certain locations. Forinstance, an event ‘landing’ happens at an airport, the exact locationof which is known. This extra location information helps to have moreaccurate decisions on event detection.

FIG. 2 shows an exemplary acceleration pattern according to theinvention. FIG. 2 actually shows the average of the acceleration signalsof the three sensors (x-, y-, and z-acceleration). The accelerationsamples are recorded during a certain period of time including the twoevents ‘taking off’ and ‘landing.’ As can be seen from the figure, thereis a discriminative difference between the pattern of accelerationduring different events of a flight.

According to the invention, pre-processing is performed on the measuredraw data. The pre-processing step is usually a digital low pass filter.

After pre-processing, feature extraction is performed. According to theinvention, feature extraction methods are used which are notcomputationally expensive considering limited computational resourcesavailable in a computational unit connected to an air cargo carrier or amobile communication device. All the feature extraction methods areapplied to a time window of acceleration signal. This window can be 1-2seconds long. The value of extracted features is averaged over samplesin each window. Adjacent windows can have overlaps up to 80%.

Acceleration Based Features:

The following 4 types of features are preferred according to theinvention:

-   1. Acceleration magnitude over different axis: This is absolute    value of acceleration over different x, y, and z axis, i.e. |a_(x)|,    |a_(y)|, |a_(z)|, where a indicates acceleration-   2. Rate of change of acceleration over different axis: This is    derivate of acceleration signal (over different axis) with respect    to time, i.e da_(x)/dt, da_(y)/dt, da_(z)/dt.-   3. Absolute magnitude of acceleration: This is defined as:    a=√{square root over (a_(x) ²+a_(y) ²+a_(z) ²)}-   4. Pairwise difference between acceleration magnitudes along    different axis, i.e. |a_(x)−a_(y)|, |a_(x)−a_(z)|, |a_(y)−a_(z)|

However, it is also preferred that combinations or even all of thesetypes are applied together. Most preferred are features 2 or 3.

In addition to the above main features, some variants can also be usedin feature extraction.

The process according to the present invention is preferably employed asfollows. Once the computational unit or the mobile communication deviceis switched on or even if the functionality according to the inventionis initiated separately, the acceleration sensor(s) measure(s) theacceleration values. This may be performed continuously orintermittently. A data processor receives the detected signals from thesensor(s), and performs feature extraction. Based at least in part orentirely on the detected acceleration parameters, filtered or “cleaned”acceleration signals are calculated in that redundant information isremoved from the signals, as also described above. These data are thensupplied to the event classification component of the processor. Here,the acceleration patterns are compared with statistical reference modelsstored in a memory. The result of such comparison process is thedetermination of a specific event reflecting the current situation ofthe air cargo carrier or airplane.

Although the present invention has been described with reference tovarious embodiments and sequences, other embodiments are considered tobe within the scope of the following claims. For example, while theinvention has been illustrated and described in detail in the drawingsand description, such illustration and description are to be consideredillustrative or exemplary and not restrictive. It will be understoodthat changes and modifications may be made by those of ordinary skillwithin the scope of the following claims. In particular, the presentinvention covers further embodiments with any combination of featuresfrom different embodiments described above and below.

Furthermore, in the claims the word “comprising” does not exclude otherelements or steps, and the indefinite article “a” or “an” does notexclude a plurality. A single unit may fulfil the functions of severalfeatures recited in the claims. The terms “essentially,” “about,”“approximately” and the like in connection with an attribute or a valueparticularly also define exactly the attribute or exactly the value,respectively. Any reference signs in the claims should not be construedas limiting the scope.

1. A method for detecting airplane flight events using at least oneacceleration sensor associated with a mobile communication device, themethod comprising the steps of: obtaining at least one accelerationsignal from said at least one acceleration sensor; pre-processing saidobtained at least one acceleration signal to remove redundantinformation present in said at least one acceleration signal; performinga feature extraction on said pre-processed at least one accelerationsignal; and classifying airplane flight events based on an accelerationpattern represented by the extracted acceleration features.
 2. Themethod of claim 1, wherein the step of classifying airplane flightevents comprises comparing said acceleration pattern with statisticalreference models for different events.
 3. The method of claim 2, whereinsaid statistical model is an artificial neural network or a Gaussianmixture model.
 4. The method of claim 1, wherein the step of classifyingairplane flight events comprises comparing said acceleration patternwith a hidden Markov model, wherein each state of the model models anevent class.
 5. The method of claim 1, wherein features extracted fromsaid pre-processed at least one acceleration signal are include at leastone of an acceleration magnitude over different axes, a rate of changein acceleration over different axis, an absolute magnitude of theacceleration, and a pairwise difference between acceleration magnitudesover different axis.
 6. The method of claim 1, further comprising thestep of changing an operating state of the mobile communication devicedepending on the classification result.
 7. The method of claim 6,wherein the mobile communication device is turned on or off to adapt themobile communication device to the detected airplane flight event. 8.The method of claim 1, further comprising the step of comparing thedetected airplane flight event with one or more regular airplane flightevents.
 9. The method of claim 8, wherein taking off or landing isdetected if the result of the comparing step is a match below apredetermined threshold between the detected airplane flight event andthe one or more regular airplane flight events.
 10. The method of claim2, wherein the statistical reference models are trained for typicalairplane flight events except for taking off and landing.
 11. A mobilecommunication device comprising: a processor; and at least oneacceleration sensor configured to provide at least one accelerationsignal to the processor; the processor being configured to pre-processsaid at least one acceleration signal to remove redundant informationpresent in said at least one acceleration signal, the processor beingfurther configured to perform a feature extraction on said pre-processedat least one acceleration signal, and the processor being configured toclassify airplane flight events based on an acceleration patternrepresented by the extracted acceleration features.
 12. A computationalunit connectable with a mobile communication device, the computationalunit for detecting airplane flight events, the computational unitcomprising: a processor; at least one acceleration sensor configured toprovide at least one acceleration signal to the processor; the processorbeing configured to pre-process said at least one acceleration signal toremove redundant information present in said at least one accelerationsignal, the processor being further configured to perform a featureextraction on said pre-processed at least one acceleration signal; andthe processor being configured to classify airplane flight events basedon an acceleration pattern represented by the extracted accelerationfeatures; and a controller configured to receive information from theprocessor and to initiate transmission of a control command to themobile communication device once connected with the computational unit,based on the detected airplane flight event.
 13. The computational unitof claim 12 wherein the computation unit is connected to a mobilecommunication device.
 14. An air cargo carrier comprising the mobilecommunication device of claim
 11. 15. An air cargo carrier comprisingthe computational unit of claim
 12. 16. The method of claim 1 whereinthe flight event include at least one of a takeoff and landing.